Isaac Chung
I lead applied research efforts for our EMEA team to scale AI for production loads at Clarifai. My team has been solving search/ranking, retrieval, and multimodal problems. Previously I was leading the effort in developing custom ML solutions for enterprise customers.
https://github.com/huggingface/transformers
https://github.com/pandas-dev/pandas
https://github.com/langchain-ai/langchain
https://github.com/run-llama/llama_index
Sessions
Developer tools power many LLM-based chat and Retrieval Augmented Generation applications today. However, there is a non-trivial knowledge barrier for entrants that could hinder developer experience. Our discussion intends to offer actionable insights into building and maintaining generative AI solutions in a secure and economical way, thereby improving the developer experience in this Generative AI wave.
Large language models (LLMs) often require huge compute resources to serve. This is a common challenge for those who want to avoid sharing their data with cloud API providers, or to deploy their stack in air-gapped environments. We will take a look at how the open source llama-cpp-python library opens the door to lower hardware requirements and simplifies deployment significantly.